Life-Cycle Inventory of Energy Use and Greenhouse Gas Emissions for Two Hydropower Projects in China
Why this work is in the frame
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Bibliographic record
Abstract
Two different sized hydropower projects in China, one with a capacity of 44MW and the other of 3,600MW, were examined through life-cycle assessment (LCA) from the perspective of both sustainability and environmental impact and the influence of project scale. Using the economic input-output based LCA approach, energy use and greenhouse gas (GHG) emissions were quantified. The resulting energy payback ratios were found to be 7 and 48, whereas the normalized GHG emissions were 44 and 6gCO2 equivalent per kW h of electricity production, both in favor of the larger project. Compared with published data on other renewable and nonrenewable options, temperate hydropower, particularly large hydropower, is indicated as an efficient electrical source with relatively low GHG emissions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it